Run iterative lasso on the MCW data set. The iterative lasso code was adapted from knowledge and concepts lab github, which can be retrieved from this link: https://github.com/UWKCL/iterativelasso
intro the MCW dataset:
1200 rows
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1-900: TrueWords
-
901 - 1200: non-words
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1-94: TrueAnimals
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95-240: TrueArtifacts
rowfilter and colfilter are filtered data. In particular, outlying voxels and voxels that never active (always 0) were removed.
Goal:
- classifcation: animals vs. artifacts, animals vs. non-animal words, animals vs. everything else, artifacts vs. non-artifacts words, etc.
notes 6/1:
data for subject1:
- X = numItems x numVoxels, matrix
- Y = numItems x 1, logical vector (e.g. 1 = 'animal', 0 = 'non-animal')
obejctive: Yhat = XB close to Y
- Since we are using LASSO, many elements in B are going to be zero out
lambda: sparsity parameter.
- bigger lambda ~> more zeros
- note that the "best lambda" is unknown
outer k folds cross validation
- maximize the "performance" on the test set
inner k folders cross validation
- tune lambda with respect to the "performance"
steps: e.g.
- for every subjects:
- do 10 folds CV
- with in each fold, try lambda1, lambda2, ..., lambdaN
- AND for each lambda, do 9 folds CV with cvglmnet()